Does this skill support real-time pricing updates?

Yes, it provides guidance on using the LiteLLM pricing database and OpenRouter API to fetch and cache current pricing programmatically.

Can I add custom models with this skill?

Absolutely. The skill includes a manual override workflow for adding custom or internal organization models that aren't listed in public databases.

What files does this skill help maintain?

It focuses on maintaining the capabilities registry (models and features) and the token manager (pricing and context limits) within the MassGen backend.

How do I ensure the model registry changes are correct?

The skill includes specific pytest commands to validate the backend capabilities and token manager logic after any updates.

AI Model Registry Maintainer

Name: AI Model Registry Maintainer
Author: Activer007

byActiver007

데이터 과학 및 ML

Manages and updates AI model registries, pricing data, and feature capabilities within the MassGen backend ecosystem.

소개

The Model Registry Maintainer skill provides a structured framework for managing the lifecycle of AI models within complex backends. It guides developers through the process of updating model metadata, including release dates, token pricing, and context window limits across key configuration files. By utilizing integration patterns for LiteLLM and OpenRouter, the skill ensures that applications remain synchronized with the latest provider releases from OpenAI, Anthropic, Google, and xAI while offering robust validation workflows to prevent configuration errors.

주요 기능

Standardized workflows for adding new LLM providers and models
Automated documentation generation for backend model tables
Pre-defined validation tests for token management and backend registry integrity
0 GitHub stars
Registry management for model-specific capabilities like vision and web search
LiteLLM integration for automated pricing and context window updates

사용 사례

Onboarding newly released AI models into production environments
Maintaining internal model registries for organizational LLM deployments
Updating API token pricing and context limits after provider announcements

소개

주요 기능

Standardized workflows for adding new LLM providers and models
Automated documentation generation for backend model tables
Pre-defined validation tests for token management and backend registry integrity
0 GitHub stars
Registry management for model-specific capabilities like vision and web search
LiteLLM integration for automated pricing and context window updates

사용 사례

Onboarding newly released AI models into production environments
Maintaining internal model registries for organizational LLM deployments
Updating API token pricing and context limits after provider announcements